Tutorial: Improving Wireless Next-Generation Industrial IoT (IIoT) Networks with Reinforcement Learning
The Industrial IoT (IIoT) paradigm is revolutionizing industrial manufacturing by combining advanced Information and Communication Technologies (ICT) with Artificial Intelligence (AI) to optimize industrial processes. Future industries will move away from traditional reliance on wired communication technologies, embracing wireless solutions that allow unprecedented levels of flexibility and adaptability. Specifically, industrial assets (robotic arms, pumps, valves, pistons, etc.) will be equipped with wireless nodes, enabling a multitude of new applications, such as real-time monitoring, autonomous robotics, and predictive maintenance. These applications will have highly heterogeneous characteristics in terms of traffic types, number and type of devices, service area, device distribution, mobility, and so on. In addition to the heterogeneity challenge, several new industrial applications are characterized by extremely stringent Quality of Service (QoS) requirements in terms of end-to-end latency and reliability. Since existing wireless technologies cannot meet these requirements, various AI-based technological advancements have been proposed in both academia and industry. From Release 16, the 3GPP introduced the Non-Public Network (NPN) paradigm, which enables new AI based solutions tailored for industrial use cases, paving the way for new research trends oriented toward 6th Generation (6G) networks. In this context, Reinforcement Learning (RL) allows networks to dynamically learn and adapt to the industrial environment and the QoS requirements of industrial applications without constant human supervision or the use of expensive datasets.
Tutorial Program
The tutorial is organized into five parts:
- Definition of industrial use cases and QoS requirements as per 3GPP and 5G-ACIA;
- 3GPP-defined NPN industrial network architectures;
- Overview of single-agent and multi-agent reinforcement learning frameworks and their importance in sixth generation wireless networks;
- Case Study 1: RL-based radio resource scheduling for IIoT networks;
- Case Study 2: multi-agent RL (MARL)-based medium access control (MAC) protocol learning for IIoT networks.
Only basic understanding of Machine Learning are expected.
